Updated 05/11/2020 # Mapping distribution of essential workers by block group for San Jose Analysis: Using the CA Essential Business Guidelines,the LODES dataset and the QWI dataset, the % of essential workers (by NAICS code) was mapped to each block group in San Jose.

Methodology

The data used to assess the % of workers deemed essential was determined using the following approach. The CA Essential proportion of 6-digit NAICS codes in each 4-digit NAICS was assessed here using a data set prepared by the CEE 218Z Vulnerability Team (linked here:https://docs.google.com/spreadsheets/d/1piMnUpohkY-HquAhuKEzeJVhCdmxTX75b8S6mnNjxQY/edit#gid=0). The CEE 218Z team read through the “Essential Critical Infrastructure Order” March 22nd 2020 and manually assigned the “essential”/“nonessential” designation by reading the order and control “F” searching through the list of 6-digit NAICS codes. There were two methods of analysis. Method 1: 4-digit NAICS codes were weighted by fraction of 6-digit NAICS codes in the 4-digit NAICS code. Then this fraction was multiplied by the number of employees in each 4-digit NAICS code (using LODES data). Then proportion of essential workers in each 2-digit NAICS code was calculated by dividing number of essential employees in each 4-digit NAICS code by the number of total employees.

Method 2: Only 2-digit NAICS codes with >= 50% of the 6-digit sub-codes were marked as essential.

Following calculation of the fraction of essential workers in each block group. It was mapped using leaflet.

First get the block groups in San Jose

## Reading layer `CSJ_Census_Tracts' from data source `/Users/spencerrobinson/pCloud Drive/SFBI/Data Library/San_Jose/CSJ_Census_Tracts/CSJ_Census_Tracts.shp' using driver `ESRI Shapefile'
## Simple feature collection with 219 features and 9 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 6112856 ymin: 1869687 xmax: 6255982 ymax: 1996555
## CRS:            2227
## Reading layer `CITY_COUNCIL_DISTRICTS' from data source `/Users/spencerrobinson/pCloud Drive/SFBI/Data Library/San_Jose/City Council Districts/CITY_COUNCIL_DISTRICTS.shp' using driver `ESRI Shapefile'
## Simple feature collection with 10 features and 7 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 6112856 ymin: 1869687 xmax: 6255982 ymax: 1996555
## CRS:            2227

Next obtain the distribution of workers from the LODES dataset

Get QWI Data which tells us number of works in each 4 digit NAICS code

## # A tibble: 268 x 5
## # Groups:   year, industry [268]
##    year  industry label                                               EmpS EarnS
##    <chr> <chr>    <chr>                                              <dbl> <dbl>
##  1 2017  5415     Computer Systems Design and Related Services       74335 15200
##  2 2017  7225     Restaurants and Other Eating Places                54063  2217
##  3 2017  5191     Other Information Services                         45339 34325
##  4 2017  3341     Computer and Peripheral Equipment Manufacturing    42593 21186
##  5 2017  3344     Semiconductor and Other Electronic Component Manu… 38376 19610
##  6 2017  6111     Elementary and Secondary Schools                   36927  5059
##  7 2017  6221     General Medical and Surgical Hospitals             31975  8206
##  8 2017  6241     Individual and Family Services                     24505  1623
##  9 2017  6113     Colleges, Universities, and Professional Schools   23194  8034
## 10 2017  5112     Software Publishers                                21594 26285
## # … with 258 more rows

Get Essential Deisgnations

Spencer Method 1: Use CA Essential 6-digit NAICS codes to assess Fraction-A: Fraction of 6-digit NAICS codes in each 4-digit NAICS code. Use QWI data to assess Fraction-B: Fraction of 4-digit NAICS code workers in each 2-digit NAICS code category (corresponds to LODES data). To determine % essential workers we multiply Fraction-A by Fraction-B.

Simone’s Method 1 with DE Essential Business Data: Use the QWI data to find the distribution of workers in NAICS 4-digit codes in Santa Clara County, and use this to weight the contributions to each 2-digit code. Note that this method disregards information about 6 digit codes within the 4 digit code that differ from the broader 4 digit code; this could be adjusted, but without data on the distribution of 6 digit within 4 digit I thought it was best to leave out.

Map of method 1 using California Essential Business Designations

Map of method 1 using Delaware Essential Business Designations

Spencer Method 2: Only count as essential the 2 digit NAICS codes with >=50% of the 6 digit sub-codes marked as essential

Map of method 2 using California Essential Business Designations